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Interview: Daniel Schiff and Yu Lu
Get PDFDr. Daniel Schiff is an assistant professor of technology policy in Purdue University’s Department of Political Science and the co-director of the Governance and Responsible AI Lab (GRAIL). Undergraduate student researcher Yu Lu, an undergraduate research affiliate with GRAIL, is interviewed along with Schiff
Selected U.S. Government Statistical Agencies and Their Data
No full textProvides introductory and detailed overview of U.S. Government agencies statistics. Includes examples of how an individual U.S. law authorizes government agencies to compile statistics for particular congressional committees and the general public. Statistics cover topics such as demography, defense, foreign assistance, economic growth, criminal justice, energy, Social Security, and Medicare
Discursive Practices in Recurring Asynchronous Writing Center Consultations
Get PDFThis study explores the discursive practices the researcher utilizes during recurring asynchronous writing consultations to engender mutually adjusted and context-driven interactions meaningful to writers’ development during virtual tutoring. While earlier studies have critiqued asynchronous tutoring for its inability to efficiently promote the writing center philosophy, the inevitability of writing centers’ transition to online modes due to the global COVID-19 pandemic warrants that writing center scholarship rethink the effectiveness of these online spaces. This study utilizes a discourse-analytic approach to analyze textual data collected from both WCONLINE and drafts I, the tutor, worked on. Individual interviews are also collected to ascertain writers’ perception of recurring asynchronous writing consultations as conversational. Textual analysis reveals that conversations occur in recuring asynchronous writing consultations on three contextual layers: first is the opening phase; second is the dialogic phase; and third is the closing phase. Interview data also shows that participants perceive their asynchronous sessions as conversational as those sessions not only function to inform, elicit, direct, and suggest, but also promote familiar relationships and provide affirmations. The study concludes by offering recommendations on how to retool the asynchronous writing consultation as not a lesser appointment option but a different option with the same opportunity as traditional writing consultation
Mechanics Cognitive Diagnostic: Mathematics skills tested in introductory physics courses
Get PDFPhysics instructors and education researchers use research-based assessments (RBAs) to evaluate students\u27 preparation for physics courses. This preparation can cover a wide range of constructs including mathematics and physics content. Using separate mathematics and physics RBAs consumes course time. We are developing a new RBA for introductory mechanics as an online test using both computerized adaptive testing and cognitive diagnostic models. This design allows the adaptive RBA to assess mathematics and physics content knowledge within a single assessment. In this article, we used an evidence-centered design framework to inform the extent to which our models of skills students develop in physics courses fit the data from three mathematics RBAs. Our dataset came from the LASSO platform and includes 3,491 responses from the Calculus Concept Assessment, Calculus Concept Inventory, and Pre-calculus Concept Assessment. Our model included five skills: apply vectors, conceptual relationships, algebra, visualizations, and calculus. The deterministic inputs, noisy \u27and\u27 gate\u27\u27 (DINA) analyses demonstrated a good fit for the five skills. The classification accuracies for the skills were satisfactory. Including items from the three mathematics RBAs in the item bank for the adaptive RBA will provide a flexible assessment of these skills across mathematics and physics content areas that can adapt to instructors\u27 needs
Anomaly Detection in Traffic Patterns Using the INDOT Camera System
Get PDFThe Transportation and Autonomous Systems Institute (TASI) of Purdue University Indianapolis (PUI) and the INDOT Traffic Management Center worked together to develop a system that monitors traffic conditions using INDOT CCTV video feeds. Computer vision-based traffic anomaly detection has been studied for the past 20 years, and a thorough state-of-the-art analysis was produced in a recent survey paper. Although AI has contributed to improving anomaly detection, several major challenges remain, such as tracking errors, illumination, weather, occlusion handling, camera pose, and perspective. In addition, the lack of real-life datasets makes the effectiveness of anomaly detection techniques unclear. This project builds on previous research by using automatic anomaly detection and AI algorithms to identify anomalous behavior of the short- and long-term variations of traffic patterns. The research team designed the new system, including the hardware and software components; the existing INDOT CCTV system; the database structure for traffic data extracted from the videos; and a user-friendly web-based server for showing the anomalies automatically
How Genre-Trained Tutors Affect Student Writing and Perceptions of the Writing Center
Get PDFWriting center scholars have long debated whether writers are best served by “generalist” tutors trained in writing center pedagogy or “specialist” tutors with insider knowledge about a course’s content or discipline-specific discourse conventions. A potential compromise that has emerged is training tutors in the purposes and features of specific genres. The writing center literature showcases many different approaches to genre training. However, little empirical research, if any, has explored how tutors’ genre knowledge affects session outcomes. The present study used a mixed-methods approach to compare session outcomes for students who worked with generalist and genre-trained tutors. We analyzed pre-consultation and revised literature review drafts to determine whether students who worked with tutors trained in the genre of literature reviews improved their drafts more or revised their drafts differently than students who worked with generalist tutors. Additionally, we performed a qualitative analysis of student reflections about their writing processes to explore how tutor training impacts students’ impressions of their consultations. Findings indicated that students who worked with genre-trained tutors revised their drafts more substantively than did students who worked with generalist tutors. Moreover, students who worked with genre-trained tutors left with notably better and richer impressions of their consultations
Automated Hot-Cycle Calorimeter for Household Refrigerating Compressors with Surrounding Air and Refrigerant Inlet Temperatures at 16 and 32°C
Get PDFFusing Classic Motion Energy Models and Deep Learning for Coarse-to-fine Moving Object Segmentation
Get PDFClassic motion energy models are able to predict a wide range of physiological and behavioral aspects of motion perception in humans. Whether these models can be used as a basis for higher-level tasks, such as moving object segmentation, has however hardly been explored yet. Here, we present a model that combines a motion energy representation with recent computer vision approaches for figure-ground segmentation of naturalistic stimuli. We find that unlike established motion segmentation models but similar to humans, our model generalizes to random-dot stimuli when only trained on RGB videos
